In [22]:
image_np_array = preprocess_original_image('Airport')
Template Credit: Adapted from an Object Detection tutorial on TensorFlow.org.
SUMMARY: This project aims to construct an object detection model using the TensorFlow-based neural network and document the end-to-end steps using a template.
Adapted from the Original Script Location: https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/tf2_object_detection.ipynb
This iteration will use the TF2 Mask R-CNN Inception ResNet V2 1024x1024 object detection model to test some sample images. The model was constructed using the Mask R-CNN Object detection model and trained on COCO 2017 dataset with training images scaled to 1024x1024.
Images Used: 1. Airport runway; 2. Tanzania Safari; 3. Streets with Cars; 4. Public Library
Dataset ML Model: Image Object Detection using TensorFlow Hub Models
Additional References: https://tfhub.dev/s?module-type=image-object-detection
# Install the packages to support accessing environment variable and SQL databases
# !pip install python-dotenv PyMySQL boto3
# Retrieve GPU configuration information from Colab
gpu_info = !nvidia-smi
gpu_info = '\n'.join(gpu_info)
if gpu_info.find('failed') >= 0:
print('Select the Runtime → "Change runtime type" menu to enable a GPU accelerator, ')
print('and then re-execute this cell.')
else:
print(gpu_info)
Mon Dec 27 18:26:25 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 495.44 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |
| N/A 38C P8 28W / 149W | 0MiB / 11441MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
# Retrieve memory configuration information from Colab
from psutil import virtual_memory
ram_gb = virtual_memory().total / 1e9
print('Your runtime has {:.1f} gigabytes of available RAM\n'.format(ram_gb))
if ram_gb < 20:
print('To enable a high-RAM runtime, select the Runtime → "Change runtime type"')
print('menu, and then select High-RAM in the Runtime shape dropdown. Then, ')
print('re-execute this cell.')
else:
print('You are using a high-RAM runtime!')
Your runtime has 13.6 gigabytes of available RAM To enable a high-RAM runtime, select the Runtime → "Change runtime type" menu, and then select High-RAM in the Runtime shape dropdown. Then, re-execute this cell.
# Retrieve CPU information from the system
ncpu = !nproc
print("The number of available CPUs is:", ncpu[0])
The number of available CPUs is: 2
import os
import pathlib
from datetime import datetime
import matplotlib
import matplotlib.pyplot as plt
import io
import scipy.misc
import numpy as np
from six import BytesIO
from PIL import Image, ImageDraw, ImageFont
from six.moves.urllib.request import urlopen
import tensorflow as tf
import tensorflow_hub as hub
# Print Tensorflow version
print(tf.__version__)
tf.get_logger().setLevel('ERROR')
# Check available GPU devices.
print("The following GPU devices are available: %s" % tf.test.gpu_device_name())
2.7.0 The following GPU devices are available: /device:GPU:0
# Begin the timer for the script processing
start_time_script = datetime.now()
# Clone the tensorflow models repository
!git clone --depth 1 https://github.com/tensorflow/models
Cloning into 'models'... remote: Enumerating objects: 3219, done. remote: Counting objects: 100% (3219/3219), done. remote: Compressing objects: 100% (2732/2732), done. remote: Total 3219 (delta 863), reused 1352 (delta 443), pack-reused 0 Receiving objects: 100% (3219/3219), 33.42 MiB | 18.86 MiB/s, done. Resolving deltas: 100% (863/863), done.
%%bash
sudo apt install -y protobuf-compiler
cd models/research/
protoc object_detection/protos/*.proto --python_out=.
cp object_detection/packages/tf2/setup.py .
python -m pip install .
Reading package lists...
Building dependency tree...
Reading state information...
protobuf-compiler is already the newest version (3.0.0-9.1ubuntu1).
0 upgraded, 0 newly installed, 0 to remove and 37 not upgraded.
Processing /content/models/research
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from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils
from object_detection.utils import ops as utils_ops
%matplotlib inline
PATH_TO_LABELS = './models/research/object_detection/data/mscoco_label_map.pbtxt'
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
def load_image_into_numpy_array(path):
"""Load an image from file into a numpy array.
Puts image into numpy array to feed into tensorflow graph.
Note that by convention we put it into a numpy array with shape
(height, width, channels), where channels=3 for RGB.
Args:
path: the file path to the image
Returns:
uint8 numpy array with shape (img_height, img_width, 3)
"""
image = None
if(path.startswith('http')):
response = urlopen(path)
image_data = response.read()
image_data = BytesIO(image_data)
image = Image.open(image_data)
else:
image_data = tf.io.gfile.GFile(path, 'rb').read()
image = Image.open(BytesIO(image_data))
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(1, im_height, im_width, 3)).astype(np.uint8)
def preprocess_original_image(selected_image, flip_image_horizontally=False,
convert_image_to_grayscale=False):
# selected_image = 'Naxos Taverna'
# flip_image_horizontally = False
# convert_image_to_grayscale = False
image_path = IMAGES_FOR_TEST[selected_image]
image_np = load_image_into_numpy_array(image_path)
# Flip horizontally
if(flip_image_horizontally):
image_np[0] = np.fliplr(image_np[0]).copy()
# Convert image to grayscale
if(convert_image_to_grayscale):
image_np[0] = np.tile(np.mean(image_np[0], 2, keepdims=True), (1, 1, 3)).astype(np.uint8)
plt.figure(figsize=(24,32))
plt.imshow(image_np[0])
plt.show()
return(image_np)
def run_detector(image_np):
# running inference
results = hub_model(image_np)
# different object detection models have additional results
# all of them are explained in the documentation
result = {key:value.numpy() for key,value in results.items()}
print(result.keys())
return result
def process_detection_result(image_np, result):
label_id_offset = 0
image_np_with_detections = image_np.copy()
# Use keypoints if available in detections
keypoints, keypoint_scores = None, None
if 'detection_keypoints' in result:
keypoints = result['detection_keypoints'][0]
keypoint_scores = result['detection_keypoint_scores'][0]
viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_detections[0],
result['detection_boxes'][0],
(result['detection_classes'][0] + label_id_offset).astype(int),
result['detection_scores'][0],
category_index,
use_normalized_coordinates=True,
max_boxes_to_draw=200,
min_score_thresh=.30,
agnostic_mode=False,
keypoints=keypoints,
keypoint_scores=keypoint_scores,
keypoint_edges=COCO17_HUMAN_POSE_KEYPOINTS)
plt.figure(figsize=(24,32))
plt.imshow(image_np_with_detections[0])
plt.show()
def process_model_with_masks(image_np, result):
# Handle models with masks
label_id_offset = 0
image_np_with_mask = image_np.copy()
if 'detection_masks' in result:
# we need to convert np.arrays to tensors
detection_masks = tf.convert_to_tensor(result['detection_masks'][0])
detection_boxes = tf.convert_to_tensor(result['detection_boxes'][0])
# Reframe the bbox mask to the image size.
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
detection_masks, detection_boxes,
image_np.shape[1], image_np.shape[2])
detection_masks_reframed = tf.cast(detection_masks_reframed > 0.5,
tf.uint8)
result['detection_masks_reframed'] = detection_masks_reframed.numpy()
viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_mask[0],
result['detection_boxes'][0],
(result['detection_classes'][0] + label_id_offset).astype(int),
result['detection_scores'][0],
category_index,
use_normalized_coordinates=True,
max_boxes_to_draw=200,
min_score_thresh=.30,
agnostic_mode=False,
instance_masks=result.get('detection_masks_reframed', None),
line_thickness=8)
plt.figure(figsize=(24,32))
plt.imshow(image_np_with_mask[0])
plt.show()
ALL_MODELS = {
'CenterNet HourGlass104 512x512' : 'https://tfhub.dev/tensorflow/centernet/hourglass_512x512/1',
'CenterNet HourGlass104 Keypoints 512x512' : 'https://tfhub.dev/tensorflow/centernet/hourglass_512x512_kpts/1',
'CenterNet HourGlass104 1024x1024' : 'https://tfhub.dev/tensorflow/centernet/hourglass_1024x1024/1',
'CenterNet HourGlass104 Keypoints 1024x1024' : 'https://tfhub.dev/tensorflow/centernet/hourglass_1024x1024_kpts/1',
'CenterNet Resnet50 V1 FPN 512x512' : 'https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512/1',
'CenterNet Resnet50 V1 FPN Keypoints 512x512' : 'https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512_kpts/1',
'CenterNet Resnet101 V1 FPN 512x512' : 'https://tfhub.dev/tensorflow/centernet/resnet101v1_fpn_512x512/1',
'CenterNet Resnet50 V2 512x512' : 'https://tfhub.dev/tensorflow/centernet/resnet50v2_512x512/1',
'CenterNet Resnet50 V2 Keypoints 512x512' : 'https://tfhub.dev/tensorflow/centernet/resnet50v2_512x512_kpts/1',
'EfficientDet D0 512x512' : 'https://tfhub.dev/tensorflow/efficientdet/d0/1',
'EfficientDet D1 640x640' : 'https://tfhub.dev/tensorflow/efficientdet/d1/1',
'EfficientDet D2 768x768' : 'https://tfhub.dev/tensorflow/efficientdet/d2/1',
'EfficientDet D3 896x896' : 'https://tfhub.dev/tensorflow/efficientdet/d3/1',
'EfficientDet D4 1024x1024' : 'https://tfhub.dev/tensorflow/efficientdet/d4/1',
'EfficientDet D5 1280x1280' : 'https://tfhub.dev/tensorflow/efficientdet/d5/1',
'EfficientDet D6 1280x1280' : 'https://tfhub.dev/tensorflow/efficientdet/d6/1',
'EfficientDet D7 1536x1536' : 'https://tfhub.dev/tensorflow/efficientdet/d7/1',
'SSD MobileNet v2 320x320' : 'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2',
'SSD MobileNet V1 FPN 640x640' : 'https://tfhub.dev/tensorflow/ssd_mobilenet_v1/fpn_640x640/1',
'SSD MobileNet V2 FPNLite 320x320' : 'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_320x320/1',
'SSD MobileNet V2 FPNLite 640x640' : 'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_640x640/1',
'SSD ResNet50 V1 FPN 640x640 (RetinaNet50)' : 'https://tfhub.dev/tensorflow/retinanet/resnet50_v1_fpn_640x640/1',
'SSD ResNet50 V1 FPN 1024x1024 (RetinaNet50)' : 'https://tfhub.dev/tensorflow/retinanet/resnet50_v1_fpn_1024x1024/1',
'SSD ResNet101 V1 FPN 640x640 (RetinaNet101)' : 'https://tfhub.dev/tensorflow/retinanet/resnet101_v1_fpn_640x640/1',
'SSD ResNet101 V1 FPN 1024x1024 (RetinaNet101)' : 'https://tfhub.dev/tensorflow/retinanet/resnet101_v1_fpn_1024x1024/1',
'SSD ResNet152 V1 FPN 640x640 (RetinaNet152)' : 'https://tfhub.dev/tensorflow/retinanet/resnet152_v1_fpn_640x640/1',
'SSD ResNet152 V1 FPN 1024x1024 (RetinaNet152)' : 'https://tfhub.dev/tensorflow/retinanet/resnet152_v1_fpn_1024x1024/1',
'Faster R-CNN ResNet50 V1 640x640' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_640x640/1',
'Faster R-CNN ResNet50 V1 1024x1024' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_1024x1024/1',
'Faster R-CNN ResNet50 V1 800x1333' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_800x1333/1',
'Faster R-CNN ResNet101 V1 640x640' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet101_v1_640x640/1',
'Faster R-CNN ResNet101 V1 1024x1024' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet101_v1_1024x1024/1',
'Faster R-CNN ResNet101 V1 800x1333' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet101_v1_800x1333/1',
'Faster R-CNN ResNet152 V1 640x640' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet152_v1_640x640/1',
'Faster R-CNN ResNet152 V1 1024x1024' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet152_v1_1024x1024/1',
'Faster R-CNN ResNet152 V1 800x1333' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet152_v1_800x1333/1',
'Faster R-CNN Inception ResNet V2 640x640' : 'https://tfhub.dev/tensorflow/faster_rcnn/inception_resnet_v2_640x640/1',
'Faster R-CNN Inception ResNet V2 1024x1024' : 'https://tfhub.dev/tensorflow/faster_rcnn/inception_resnet_v2_1024x1024/1',
'Mask R-CNN Inception ResNet V2 1024x1024' : 'https://tfhub.dev/tensorflow/mask_rcnn/inception_resnet_v2_1024x1024/1'
}
COCO17_HUMAN_POSE_KEYPOINTS = [(0, 1), (0, 2), (1, 3), (2, 4), (0, 5),
(0, 6), (5, 7), (7, 9), (6, 8), (8, 10),
(5, 6), (5, 11), (6, 12), (11, 12),
(11, 13), (13, 15), (12, 14), (14, 16)]
model_display_name = 'Mask R-CNN Inception ResNet V2 1024x1024'
model_handle = ALL_MODELS[model_display_name]
print('Selected model:'+ model_display_name)
print('Model Handle at TensorFlow Hub: {}'.format(model_handle))
Selected model:Mask R-CNN Inception ResNet V2 1024x1024 Model Handle at TensorFlow Hub: https://tfhub.dev/tensorflow/mask_rcnn/inception_resnet_v2_1024x1024/1
print('loading model...')
hub_model = hub.load(model_handle)
print('model loaded!')
loading model... model loaded!
IMAGES_FOR_TEST = {
'Airport' : 'https://dainesanalytics.com/datasets/random-sample-images/airport-operations.jpg',
'Safari' : 'https://dainesanalytics.com/datasets/random-sample-images/tanzania-safari.jpg',
'Street' : 'https://dainesanalytics.com/datasets/random-sample-images/traffic-lights.jpg',
'Library' : 'https://dainesanalytics.com/datasets/random-sample-images/library-books.jpg',
}
image_np_array = preprocess_original_image('Airport')
detected_result = run_detector(image_np_array)
dict_keys(['final_anchors', 'rpn_box_predictor_features', 'rpn_box_encodings', 'proposal_boxes', 'image_shape', 'num_detections', 'rpn_objectness_predictions_with_background', 'detection_multiclass_scores', 'refined_box_encodings', 'raw_detection_boxes', 'anchors', 'proposal_boxes_normalized', 'detection_scores', 'detection_anchor_indices', 'rpn_features_to_crop', 'class_predictions_with_background', 'raw_detection_scores', 'box_classifier_features', 'detection_boxes', 'mask_predictions', 'detection_classes', 'detection_masks', 'num_proposals'])
process_detection_result(image_np_array, detected_result)
image_np_array = preprocess_original_image('Safari')
detected_result = run_detector(image_np_array)
dict_keys(['final_anchors', 'rpn_box_predictor_features', 'rpn_box_encodings', 'proposal_boxes', 'image_shape', 'num_detections', 'rpn_objectness_predictions_with_background', 'detection_multiclass_scores', 'refined_box_encodings', 'raw_detection_boxes', 'anchors', 'proposal_boxes_normalized', 'detection_scores', 'detection_anchor_indices', 'rpn_features_to_crop', 'class_predictions_with_background', 'raw_detection_scores', 'box_classifier_features', 'detection_boxes', 'mask_predictions', 'detection_classes', 'detection_masks', 'num_proposals'])
process_detection_result(image_np_array, detected_result)
image_np_array = preprocess_original_image('Street')
detected_result = run_detector(image_np_array)
dict_keys(['final_anchors', 'rpn_box_predictor_features', 'rpn_box_encodings', 'proposal_boxes', 'image_shape', 'num_detections', 'rpn_objectness_predictions_with_background', 'detection_multiclass_scores', 'refined_box_encodings', 'raw_detection_boxes', 'anchors', 'proposal_boxes_normalized', 'detection_scores', 'detection_anchor_indices', 'rpn_features_to_crop', 'class_predictions_with_background', 'raw_detection_scores', 'box_classifier_features', 'detection_boxes', 'mask_predictions', 'detection_classes', 'detection_masks', 'num_proposals'])
process_detection_result(image_np_array, detected_result)
image_np_array = preprocess_original_image('Library')
detected_result = run_detector(image_np_array)
dict_keys(['final_anchors', 'rpn_box_predictor_features', 'rpn_box_encodings', 'proposal_boxes', 'image_shape', 'num_detections', 'rpn_objectness_predictions_with_background', 'detection_multiclass_scores', 'refined_box_encodings', 'raw_detection_boxes', 'anchors', 'proposal_boxes_normalized', 'detection_scores', 'detection_anchor_indices', 'rpn_features_to_crop', 'class_predictions_with_background', 'raw_detection_scores', 'box_classifier_features', 'detection_boxes', 'mask_predictions', 'detection_classes', 'detection_masks', 'num_proposals'])
process_detection_result(image_np_array, detected_result)
print ('Total time for the script:',(datetime.now() - start_time_script))
Total time for the script: 0:15:28.317179